Identification of vulnerable cell types and quantification of cell type-specific differential gene expression in the Pitt-Hopkins Syndrome mouse model
Awardee: Brady Maher
Institution: Johns Hopkins University School of Medicine
Grant Amount: $73,473.00
Funding Period: February 1, 2024 - January 31, 2025
Summary:
The brain is composed of two major cell types, neurons and glia; however, these two cell classes quickly become very complex as you begin to categorize them into specific subtypes of cells based on their structure and function. For instance, glia cells are comprised of astrocytes, oligodendrocytes, and microglia, which all have unique structures and functions, and these three cell types can be further divided into additional subtypes. This complexity is even greater for neurons, which are first subdivided into excitatory and inhibitory neurons, but then have so many subtypes they cannot all be listed here. Recent technological advances in single cell sequencing (scRNAseq) have emerged that allow us to quantify the expression of genes within individual cell types, which is critically important because each subtype of cell expresses different genes that regulate the specific function of that cell type. Transcription factor 4 (Tcf4), is a transcription factor that regulates expression of specific genes, however the set of genes it regulates, differs depending on the cell type. Currently, there is no data available that describes how disease-causing mutations in TCF4 effects cell type specific expression in the brain. Therefore, we propose to perform scRNAseq on brain samples from the PTHS mouse model and WT littermates. Previous results from our lab and others have shown that Tcf4 is highly expressed during brain development and mutations in Tcf4 result in alterations in the proportions of specific types of cells (e.g., GABAergic interneurons and oligodendrocytes), however we hypothesize this list of vulnerable cell types is not complete. In addition, Tcf4 is expressed throughout the lifespan, where it is continually regulating gene expression in a cell-type dependent manner. Unfortunately, the field currently lacks an understanding of which sets of genes Tcf4 regulates in any specific cell type. Our proposal is designed to fill in these important knowledge gaps and will help to confirm prior observed vulnerable cell types and identify new cell types of risk. In addition, our approach will identify genes that are differentially expressed in specific cell types, which will help to identify genes that can be targeted by cell type-specific therapeutic approaches. We plan to openly share our results with the research community. We believe it will be beneficial to improving emerging gene therapy approaches by informing us about which cell types should be modified. We believe it will serve to identify vulnerable cell types that may in the future be replaced using cell replacement therapies. Lastly, we believe these datasets will be useful for computational approaches that are using artificial intelligence and machine learning paradigms to identify therapeutic targets and predict effective therapeutic compounds.